Undergraduate engineering students interested in research typically enroll at Georgia Tech with an eye on joining a lab within its eight schools. Their long-term goal is to write and submit a study, hoping for an eventual publication in a peer-reviewed journal.

Rohan Datta, however, reversed the usual timeline. The 18-year-old recently graduated from The Galloway School in Atlanta. By the time he attends his first classes on campus this fall as a Stamps Scholar, Datta will already have a published paper on his resume.

With guidance from and collaboration with both a professor and an alumna of the School of Materials Science and Engineering (MSE), Datta is the first author on a recently published study in the Journal of Chemical Physics. In the paper, “Conductivity prediction model for ionic liquids using machine learning,” Datta describes his construction of a deep neural network capable of making rapid and accurate predictions of the conductivity of ionic liquids.

Datta’s publication marks a fitting conclusion to high school while serving as the next phase of his Georgia Tech experience.

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